Abstract: The present disclosure relates to systems, methods, and computer-readable media for determining optimal index configurations for intelligently managing updates of virtual machines in an offline manner in a cloud computing system. For instance, a virtual machine (VM) update system can efficiently determine when to apply updates to virtual machines in an intelligent manner that prevents the updates from interfering with the deallocation of virtual machines. In addition, the VM update system can utilize the operating system (OS) disk image snapshots to automatically provide safeguards and ensure that updates do not degrade the performance of the virtual machines, or in the case of an update failure, that the virtual machines are restored to their previous state without the data loss.

Abstract: Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person's heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual's transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.

Abstract: A flexible electronic device is provided herein. The device may include a display unit having a continuous display area extending across a first section, a second section, and a transition section of the device, where the transition section is positioned between the first section and the second section. The device is configured to bend about a first axis positioned in a same plane as a center of the transition section, at a distance greater than zero from a first surface of the device positioned in the plane. The device may be configured to bend about a hinge positioned at least partially within the transition section. The device may include a sensor configured to identify a position of the first section relative to the second section of the device, and a processor configured to determine which image or images to display on the display unit based on the identified position.

Abstract: Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person's heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual's transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.

Abstract: Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person's heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual's transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.

Abstract: A flexible electronic device is provided herein. The device may include a display unit having a continuous display area extending across a first section, a second section, and a transition section of the device, where the transition section is positioned between the first section and the second section. The device is configured to bend about a first axis positioned in a same plane as a center of the transition section, at a distance greater than zero from a first surface of the device positioned in the plane. The device may be configured to bend about a hinge positioned at least partially within the transition section. The device may include a sensor configured to identify a position of the first section relative to the second section of the device, and a processor configured to determine which image or images to display on the display unit based on the identified position.

Abstract: Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person's heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual's transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.